We are on the verge of the United Nations Decade for Ecosystem Restoration. The Decade starts in 2021 and ushers in a global effort to drive ecosystem restoration to support climate mitigation and adaptation, water and food security, biodiversity conservation and livelihood development. In order to prepare for the decade, we must understand the enabling environment. However, to understand policies involves reading and analyzing thousands of pages of documentation across multiple sectors. Using NLP to mine policy documents, would promote knowledge sharing between stakeholders and enable rapid identification of incentives, disincentives, perverse incentives and misalignment between policies. If a lack of incentives or disincentives were discovered, this would provide an opportunity to advocate for positive change. Creating a systematic analysis tool using NLP would enable a standardized approach to generate data that can support evidence-based change.

The viability of Nature Based Solutions projects is often impeded by the lack of positive incentives to adopt practices that conserve or restore land. Perverse incentives also encourage business-as-usual practices that have a heavy carbon footprint, degrade ecosystems, exploit workers or fail to generate decent livelihoods for rural communities.

Shifting incentives in a specific jurisdiction begins with a diagnosis of the country’s existing regulations, incentives and mandates across agencies. The aim is to gain a thorough understanding of current regulations and incentives that are relevant to forest and landscape restoration, the reality of how they are applied in practice and the degree of alignment or conflict across ministries and different levels of government. Shifting incentives at international level, may require such diagnostics across multiple countries, or voluntary standards and business practices. For this purpose, natural language processing technologies are needed to expedite systematic review of the legal and policy context in the relevant jurisdictions, as well as examples of innovative incentives from other contexts.

The initial focus is in Latin America, therefore native or fluent Spanish speakers are required to lead the project. If volunteers are interested in other country contexts, please contact us and we will assess data availability, but Latin America is a priority focus in the first instance.

Success will be achieved as governments or market platforms create aligned incentives across sectoral silos, remove administrative bottlenecks, or reorient incentives in line with recommendations. To advocate for change, a systematic process of analyzing incentives is needed beyond manual policy analysis. Currently manual policy analysis is the only method utilized to understand incentives. This is inadequate when considering the scale of the task.

Global Restoration Initiative and Forest teams. Supporting the work of the Policy Accelerator

The src/data/reading_pdfs.py takes as input data/raw/onedrive_docs.zip and outputs data/interim/pdf_files.json To execute the script you need to have the data folder structure described in the Project Organization section with the onedrive_docs.zip under data/raw. Then, after activating the python environment, run make pdf_files from the root directory.

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py

Project based on the cookiecutter data science project template. #cookiecutterdatascience